In networks and other electronic environments, it is common for multiple users to send requests to a host, service, server, etc. As the number of users and requests increases, as well as the amount of information associated with each request, the number and/or size of resources needed to handle those requests increases as well. Unfortunately, the cost of purchasing and maintaining these resources can limit the amount of resources made available, which can reduce throughput and capacity, and can potentially cause users to move their data and/or processing to other providers.
For some users, such as enterprise users, the amount of data for any request can require a large, complicated system to process using existing approaches. For example, an enterprise user might desire to analyze information such as log, sales, and/or billing data for each previous day, which for certain enterprises might have terabytes of data. Other users might not have a large volume of data to process with each request, but may instead send a large number of requests at very high frequencies.
A conventional approach to handling such traffic involves utilizing a large enterprise database system or a data warehousing vendor that typically relies upon a proprietary hardware solution. Such approaches are expensive and limited in flexibility. Further, they do not provide scalability and flexibility with respect to the languages and protocols that can be utilized. Further, such approaches can be at least impractical for any particular user to implement on their own.